SUMMARY Control of Cutaneous Leishmaniasis (CL) in the Americas largely relies on passive case detection and ambulatory treatment without comprehensive follow-up. Information on clinical response and adverse drug reactions under the standard of care are neither routinely obtained nor reported. Passive case detection is particularly inefficient in the vast rural areas of transmission where dispersed populations have limited access to diagnosis through health systems. Despite some recent reports of treatment failures, the logistical demands of endemic areas, and the toxicity of the recommended parenterally administered drugs, have contributed to the virtual absence of information on their routine effectiveness, as opposed to efficacy. The overall goal of the proposed project is to estimate the hidden burden of cutaneous leishmaniasis by predictive risk mapping and quantifying underestimation cases and effectiveness of standard-of-care treatment. We will integrate a comprehensive assessment of under-reporting and under-ascertainment through case detection based on passive and active surveillance by community-based surveillance and risk mapping and we will implement mHealth tools for quantifying treatment effectiveness under routine clinical practice conditions. This working framework will allow us to extend mHealth surveillance to treatment follow-up and effectiveness estimation in CL. To achieve this, we will develop the following specific aims: 1) Estimate under-reporting and under-ascertainment of CL using mHealth assisted active case detection, chain-referral sampling, and community-based surveillance. 2) Develop a model, based on remote sensing and vector niches, for predicting risk of cutaneous leishmaniasis, and estimate its ability to identify high-risk sites 3) Estimate the effectiveness of the standard treatment for CL in three municipalities supported with mHealth tools managed by leading participants of the community. Results from this project will provide the evidence base for implementation of community based active case detection and referral for standard of care treatment using novel mHealth tools that can be implemented at the community level. Early diagnosis and treatment will reduce the burden of disease, and its physical and social consequences.